Abstract: The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.

Abstract: The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.

Abstract: The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between the acquired cardiac signals and photoplethysmographic signals to predict/estimate the presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between the cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of a histogram of the synchronicity between the cardiac signals and photoplethysmographic signals are evaluated.

Abstract: The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.

Abstract: The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify nonlinear dynamical properties (such as Lyapunov exponent (LE), correlation dimension, entropy (K2), or statistical and/or geometric properties derived from Poincaré maps, etc.) of biophysical signals such as photoplethysmographic signals and/or cardiac signals to predict presence and/or localization of a disease or condition, or indicator of one, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to elevated or abnormal left ventricular end-diastolic pressure disease) and pulmonary hypertension, among others.